{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Gradient Boost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Importing the model\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_corr_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='auto',\n",
       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting and training\n",
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.67      0.33      0.44         6\n",
      "         Buy       0.33      0.57      0.42         7\n",
      "        Hold       0.00      0.00      0.00         4\n",
      "\n",
      "    accuracy                           0.35        17\n",
      "   macro avg       0.33      0.30      0.29        17\n",
      "weighted avg       0.37      0.35      0.33        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'n_estimators': [50,100, 200],\n",
    "          'learning_rate': [1,0.1,.01],\n",
    "          'max_depth': [1, 3, 6],\n",
    "          'min_samples_split': [2, 5],\n",
    "          'min_samples_leaf': [1, 3, 5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.321), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.418), total=   0.1s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.358), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.231), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.418), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.356), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.231), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.418), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.356), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
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      "[CV] learning_rate=1, max_depth=1, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n"
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      "[CV]  learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=2, n_estimators=50, score=(train=1.000, test=0.214), total=   0.1s\n",
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=2, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.293), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=2, n_estimators=200 \n",
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n",
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n",
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      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
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      "[CV]  learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.262), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=3, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n",
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      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.381), total=   0.1s\n",
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      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
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      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.190), total=   0.2s\n",
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      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
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      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.538), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.449), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.170), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.538), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.449), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.211), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.538), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=1.000, test=0.429), total=   0.1s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.305), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.441), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.449), total=   0.2s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.243), total=   0.6s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.538), total=   0.3s\n",
      "[CV] learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=1, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.429), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=0.943, test=0.264), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=0.948, test=0.448), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=50, score=(train=0.893, test=0.312), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.423), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.448), total=   0.2s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=100, score=(train=0.948, test=0.344), total=   0.2s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.298), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.418), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.295), total=   0.3s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=0.943, test=0.264), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=0.948, test=0.448), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=50, score=(train=0.893, test=0.312), total=   0.1s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.423), total=   0.2s\n",
      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=100 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=1, min_samples_split=5, n_estimators=200 \n",
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      "[CV] learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n"
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      "[CV]  learning_rate=0.1, max_depth=1, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.258), total=   0.2s\n",
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      "[CV] learning_rate=0.1, max_depth=3, min_samples_leaf=1, min_samples_split=2, n_estimators=50 \n",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     "output_type": "stream",
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "  'precision', 'predicted', average, warn_for)\n"
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      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.303), total=   0.6s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.381), total=   1.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50, score=(train=0.845, test=0.191), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50, score=(train=0.885, test=0.262), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=50, score=(train=0.834, test=0.405), total=   0.3s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.302), total=   0.3s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.245), total=   0.7s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.381), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.205), total=   1.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.272), total=   1.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=3, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.381), total=   0.6s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50, score=(train=0.827, test=0.221), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50, score=(train=0.818, test=0.261), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=50, score=(train=0.743, test=0.429), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=1.000, test=0.365), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=0.928, test=0.391), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=100, score=(train=0.948, test=0.429), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.236), total=   0.5s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.313), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=2, n_estimators=200, score=(train=1.000, test=0.429), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.827, test=0.221), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.818, test=0.261), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=50, score=(train=0.743, test=0.429), total=   0.1s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=1.000, test=0.365), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.928, test=0.391), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=100, score=(train=0.948, test=0.429), total=   0.2s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.236), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.313), total=   0.4s\n",
      "[CV] learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200 \n",
      "[CV]  learning_rate=0.01, max_depth=6, min_samples_leaf=5, min_samples_split=5, n_estimators=200, score=(train=1.000, test=0.429), total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 486 out of 486 | elapsed:  1.9min finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
       "                                                  init=None, learning_rate=0.1,\n",
       "                                                  loss='deviance', max_depth=3,\n",
       "                                                  max_features=None,\n",
       "                                                  max_leaf_nodes=None,\n",
       "                                                  min_impurity_decrease=0.0,\n",
       "                                                  min_impurity_split=None,\n",
       "                                                  min_samples_leaf=1,\n",
       "                                                  min_samples_split=2,\n",
       "                                                  min_weight_fraction_leaf=0.0,\n",
       "                                                  n_estimators=100,\n",
       "                                                  n_iter_no...\n",
       "                                                  presort='auto',\n",
       "                                                  random_state=None,\n",
       "                                                  subsample=1.0, tol=0.0001,\n",
       "                                                  validation_fraction=0.1,\n",
       "                                                  verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'learning_rate': [1, 0.1, 0.01],\n",
       "                         'max_depth': [1, 3, 6], 'min_samples_leaf': [1, 3, 5],\n",
       "                         'min_samples_split': [2, 5],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#search = RandomizedSearchCV(clf, params, n_iter=20, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.9358067348978291\n",
      "Mean Testing Score: 1.0\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 1,\n",
       " 'max_depth': 6,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 50}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None, learning_rate=1,\n",
       "                           loss='deviance', max_depth=6, max_features=None,\n",
       "                           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                           min_impurity_split=None, min_samples_leaf=1,\n",
       "                           min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
       "                           n_estimators=50, n_iter_no_change=None,\n",
       "                           presort='auto', random_state=None, subsample=1.0,\n",
       "                           tol=0.0001, validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.60      0.50      0.55         6\n",
      "         Buy       0.40      0.57      0.47         7\n",
      "        Hold       0.00      0.00      0.00         4\n",
      "\n",
      "    accuracy                           0.41        17\n",
      "   macro avg       0.33      0.36      0.34        17\n",
      "weighted avg       0.38      0.41      0.39        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
